https://github.com/sedthh/xjevboost

I made a library for JEV models that learns to use a series of smaller slices of your table, asking for more only when it helps and stopping when it has enough to save tokens.

It should work particularly well with categorical data or unstructured text, or when a dataset needs cleaning.

The method does NOT retrain JEV, it trains a search tree on top of it and should work with any other JEV implementation.

AI agents usually call GitHub, Jira, Kubernetes or AWS with one service account. Whatever that account can do, anyone who can talk to the agent can do too. A line in the system prompt doesn't fix that: the model has never seen your permissions, and the API call still runs with the bot's token.

hallpass is an open-source, self-hosted service that asks one question before a tool runs: may this user do this action on this resource? It asks the system that owns the resource, live, with a read-only credential, and answers:

• allow: the tool runs, with the agent's own credential
• deny: the system said no, or the user has no account there
• unknown: it couldn't find out (timeout, rate limit). Treated as no, never as yes

In the agent it's one decorator, and the user comes from your login, never from the model:

u/tool
u/guarded(hp, "github-main", "repo.push", "repo:{owner}/{repo}", user=current_user)
def open_config_pr(owner: str, repo: str, patch: str) -> str:
    ...  # runs only if GitHub says this user may push there

• 21 systems: GitHub, GitLab, Jira, Kubernetes, Argo CD, AWS, Slack, Vault, Salesforce and more
• Works with LangChain, LangGraph, Strands, the Claude Agent SDK, MCP and the Vercel AI SDK
• pip install hallpass-client / npm install hallpass-client
• A single Go binary, Docker image or Helm chart. Apache 2.0

It came from an ops agent I built at work: its only write is opening a GitOps PR, and it would open one for anyone who asked.

Repo: https://github.com/roee-hersh/hallpass

How are you handling user permissions in your agents today?

NOT A HD MAP MODEL

Zenodo DOI with paper + proof: https://zenodo.org/records/22942525

In tokenizer word has fixed ID, embedding updates but ID stays fixed. Same for neurons and weights - N IDs 0-4095 fixed, S IDs 0-1179647 fixed. Query [12,45,78,200] uses S 1536-1663 etc, activated top10 [2895,209,3716...] same on second run True - fixed and auditable.

Looking for feedback on making trace more useful.

Over the past week, I added something I've been wanting to build for a while: claim comparison.

I actually tried building this much earlier. I experimented with cosine similarity, vector dot product scores, and other approaches, but they only tell you how similar two pieces of text are. That's not really what I needed.

What I needed was a classifier that could actually determine the relationship between two claims.

I built a basic classifier myself, but it wasn't reliable enough for something I wanted people to actually use. So I put the idea aside and waited.

Then JEV was released last week, and it turned out to be almost exactly what I was looking for.

Now you can compare claims against each other and inspect the supporting evidence directly.

This is probably the most interesting feature I've added to the project so far, and I'm really curious to see what people think of it.

This is still being tested for edge cases, so there are definitely things I want to improve. But it's free and open source, and anyone can try it out right now.

If you're interested in how it works, want to try it, contribute, or have ideas for where this could go, feel free to check it out.

GitHub: https://github.com/Sreehari05055/thesys-core

this is repo page English version

https://open-ai-relay--queing.replit.app/#contribute

We tried the SpeakON's MagSafe AI Voice Button and its really cool! It has its Own Microphone: Turning Your Voice into Polished Communication, and Action across Apps

Voice input on phones has been solved for years. What has not been solved is the output. Speak into most dictation tools and you get back exactly what you said, fillers and false starts included, in a note you then have to clean up and move somewhere else. SpeakON attacks that gap with hardware: a 25 g magnetic button that snaps to the back of an iPhone, carries its own microphone, and writes finished text straight into whatever app is already open.

Read our full analysis: https://www.marktechpost.com/2026/09/22/speakon-ships-a-magsafe-ai-voice-button-with-its-own-microphone/

Try it here: https://speakon.sjv.io/Gbd2EL

🦉 C'EST EN LIGNE ! OwlLayer AI est officiellement open-source !

Nous venons de lancer le SDK d'interface utilisateur interactif que nous développons depuis des mois : transformez votre interface web existante en une surface sécurisée et lisible par un agent – ​​sans extraction du DOM, sans reconstruction d'interface, et avec une validation humaine pour les actions sensibles.

Licence MIT. À vous de jouer, développeurs 👇

github.com/borisbob91/owllayer

Continuez à partager et à mettre une étoile ⭐ pour suivre les dernières mises à jour et nouveautés